Viable eco-efficiency targets for waste collection communities.

Waste management is crucial for advancing the circular economy, and Italy has begun to address this issue by organizing municipalities into collaborative communities of municipalities, named ATOs. In this paper, we propose a quantitative approach based on conditional efficiency analysis to estimate viable eco-efficiency targets for these waste collection communities. The proposed targets are both eco-efficient, because they reflect optimal resource allocation within the eco-efficiency framework, and viable, because they consider the unique specificities of each waste community. The methodology determines a pathway or direction for municipalities to reach the eco-efficiency frontier based on specific external factors, ensuring that each municipality is benchmarked against others with similar contexts within the same community. Our analysis focuses on 89 Italian municipalities within the ATO "Città metropolitana di Roma Capitale" in 2021, revealing that size and economic development significantly contributed to viable eco-efficiency within the community during this period. The proposed approach is general and flexible and can be applied to other municipalities in Italy or across Europe. It can also be extended to meso (regional) or macro (country) levels of analysis.

Strategies for developing sustainable communities in higher education institutions.

Higher education institutions (HEIs), based on learning, innovation, and research, can support the progress of civil society. Many HEIs are implementing sustainability practices and projects to counteract climate change, often involving youth participation. The present study aimed at identifying how sustainable communities may be fostered in a university setting. To that end, a questionnaire was administered to engineering students at the start and end of a course on energy issues, assessing their perceptions of sustainability using multi-criteria decision analysis. The results showed that students placed greater value on sustainability at the end of the course. Additionally, the findings highlight that the implementation of projects aimed at tackling real problems may be useful for disseminating knowledge and sustainable practices. The main implications of this study indicate that sustainable communities in academia lay on six foundational pillars: sustainable education, energy (and resource) independence, subsidies in support of the green economy, initiatives aimed at reducing the carbon footprint, energy community development, and new green professional opportunities.

An integrated dataset providing a landscape of the Italian Universities.

We provide data describing all the largest Italian Universities from several perspectives, including scientific research, administrative and economic point of view. In particular, associated with each University, we have the following data. (a) The list of the most representative research keywords (single words and bigrams), automatically extracted from titles, abstracts and other possible metadata of all the research publications available for that University in Scopus database at October 2022. (b) The Extended_name of the University, Status, University_Type, State_status, number of Managerial and Administrative Staff, Teaching Staff and Researchers, Phd Diplomas, Phd Enrollments, Enrolled Undergraduates, Enrolled Graduates, Graduates, Master I Lv Graduates, Enrolled Master's Degree I Lv, Master II Lv, Graduates Enrolled, Master II Lv, Graduates Specialistic Schools and Enrolled Specialistic Schools were extracted from USTAT database for the years 2016-2018. (c) The data of Educational Income, Income from Commissioned Research and Technology Transfer, Income from Research with competitive funding, Own Income, Contributions from others (private), Contributions from others (public), Contributions from universities, Contributions from the European Union and the Rest of the World, Contributions from other local governments, Contributions Regions and Autonomous Provinces, MIUR and other central government grants, Operating Costs, Current Management Costs, Managerial and administrative personnel costs, Research and teaching staff cost, Cost of Lecturers and Researchers, Cost Scientific Collaborators, Cost of Contract Teachers, Cost of Language Experts, Other research and teaching personnel costs, Personnel Costs, Scientific equipment, Concessions, licenses and trademarks, Patent rights are extracted from the unique University Balance Sheet of each university for the years 2016-2018. These data were of difficult availability; they have been extracted from several heterogeneous sources and have been automatically checked, cleaned from errors, integrated, and missing values have been imputed as much as possible. However, due to large missing portions in the sources, they still contain several missing parts. Nonetheless, they represent a powerful snapshot of the Italian Universities, and can be of interest to researchers for many analyses of the Italian academic world. All the sources of the openly available data are provided.

Determinants of the incidence of non-academic staff in European and US HEIs.

In this article, we contribute to the scant literature covering quantitative studies on the determinants of the non-academic staff incidence in higher education institutions by analysing how the proportion of non-academic staff is related to key features such as size, prestige, year of foundation and financial structure of universities. We apply nonlinear regression analysis to compare HEIs across Europe and the USA, taking into account time and cross-country heterogeneity of the two balanced panel datasets concerning European and American universities over a period of 6 years (2011-2016 for Europe and 2012-2017 for the USA). Evidence suggests that in both Europe and the USA, public and larger (if sufficiently large) as well as more research-oriented units are characterised by a higher proportion of non-academic staff. In Europe, we observe an inverted U-shaped effect of the share of non-personnel expenditure and the foundation year on the proportion of non-academic staff, while the proportion of non-academic staff decreases with the share of core and third-party funding. For the USA, we obtain similar findings except that the share of core funding and third-party funding is characterised by a U-shaped effect, and the impact of the share of non-personnel expenditure has no empirical effect on the proportion of non-academic staff. Additionally, we discover that some factors that contribute to the proportion of non-academic staff may constitute indicators of performance, suggesting the need for further research to extend our knowledge on the complex issue of the role played by non-academic staff in university performance. Supplementary Information: The online version contains supplementary material available at 10.1007/s10734-022-00819-7.

Information reconstruction in educational institutions data from the European tertiary education registry.

Universities and other organizations providing higher level education are collectively called Higher Education Institutions. Their detail data, for instance number of students, number of graduates, etc., constitute the basis for several important analyses of the educational systems. This work provides data of the European Tertiary Education Register (ETER), which describes the Educational Institutions of Europe. These data have been gathered through the National Statistical Authorities of all the Countries participant in the ETER Project. However, they include many scattered missing values. Therefore, we have developed and applied an imputation methodology (see "Imputation Techniques for the Reconstruction of Missing Interconnected Data from Higher Educational Institutions, Bruni et al. [3]) to replace the missing values with feasible values being as similar as possible to the original values that have been lost and are now unknown. Thus, we also provide the imputed version of the same dataset, which allows more in-depth analyses of the European Higher Education Institutions. Both datasets (before and after imputation) are provided in two versions: with or without bibliometric information for the Institutions, so the user can also consider these additional information if interested.

Reconstructing Nonparametric Productivity Networks.

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra- and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown.

In Defense of Merit to Overcome Merit.

Bibliometric indicators such as the number of published articles and citations received are subject to a strong ambiguity. A high numerical value of bibliometric indicators may not measure the quality of scientific production, but only a high level of activity of a researcher. There may be cases of good researchers who do not produce a high number of articles, but have few research products of high quality. The sociology of science relies on the so-called "Matthew effect," which is inspired by Matthew's Gospel on Talents. "Those that have more will have more" seems to support the idea that those that publish more, merit to have higher bibliometric indicators, and to be recognized for their major results. But is this really the case? Can bibliometric indicators be considered a measure of the merit of scholars or they come from luck and chance? The answer is of fundamental importance to identify best practices in research assessment. In this work, using philosophical argumentation, we show how Christian theology, in particular St. Thomas Aquinas, can help us to clarify the concept of merit, overcoming the conceptual ambiguities and problems highlighted by the existing literature. By doing this, Christian theology, will allow us to introduce the evaluation framework in a broader perspective better suited to the interpretation of the complexity of research evaluation.